How we understand digital literacy
Not a checklist of skills, but the capability to navigate digital environments and make informed, self-determined choices within them.
It's tempting to picture digital literacy as a checklist: can you use a search engine, change a privacy setting, spot a scam email? But in a world of generative AI, opaque algorithms, and always-on connection, that checklist is never finished and never quite fits. So we start somewhere else. We treat digital literacy as the capability to navigate digital environments and make informed, self-determined choices within them, even when those environments are designed to nudge you the other way.
Where the idea comes from
We didn't invent this idea from scratch; it grows out of roughly six decades of scholarship, and two traditions in particular. The first came from media and communication research. Already in the early 1990s, scholars defined media literacy as the ability to access, analyze, evaluate, and create media, deliberately medium-agnostic, so it would work for newspapers, television, and whatever came next, and framed around empowerment rather than fear. When the internet arrived, Paul Gilster caught the shift in a phrase that still holds up: digital literacy is about "mastering ideas, not keystrokes."
The second tradition came from research on digital inequality, the "digital divide." Its lasting insight was that giving people access is not enough: what people can actually do online, and what they get out of it, is unequally distributed and tracks social position more than age. This line of work gave the field rigorous measurement and a hard-won lesson: digital skills are not one thing. People strong in one area are often weak, and vulnerable, in another, and generic training does little to build the higher-order skills that matter most. The "digital native" who is automatically good with everything is a myth.
Over time, though, both traditions kept meeting each new technology the same way: by naming a new literacy. Privacy literacy, news literacy, advertising literacy, algorithm literacy, AI literacy, each with its own definitions and its own tests, and little to connect them. That proliferation is exactly what our model is meant to tame. Instead of inventing a new literacy for every wave of technology, we separate the two questions these labels quietly mix up (what a person can do, and where they are doing it) and cross them.
A map, not a checklist
Underneath that capability sits a simple structure. There are four core competencies, the things a person can actually do: operate the technology, find and judge information, communicate and interact, and create content. And there are the domains where those skills matter, from everyday computer and internet use to privacy, news and misinformation, advertising, artificial intelligence, algorithms, and digital well-being. Crossing the two gives a map of 28 distinct, measurable skills, one competency applied within one domain. Adjusting your privacy settings and steering what an algorithm shows you are both "technical" skills, but they aren't the same skill, and people are rarely equally good at both.
The digital literacy model
Hover or tap a cell to explore this competency × domain
Four competencies across seven domains. Hover a cell to see how a skill plays out in that context.
The four competencies
What a person can do: the same four abilities, whatever the technology. These change slowly.
- Technical & Operational Skills: Actually operating digital tools, platforms and settings, from finding a function to switching it on and adjusting it. For example: turning on two-factor authentication (Privacy), installing an ad blocker (Advertising), or setting app time limits (Well-Being).
- Information Navigation & Processing: Finding information, placing it in context, and judging how reliable it is. For example: spotting a sensationalized headline (News), recognizing a phishing message (Privacy), or checking whether a chatbot's sources actually exist (Generative AI).
- Communication & Interaction: How you behave, express yourself and deal with others in digital spaces. For example: choosing the right channel for a message (Computer & Internet), weighing what to share with whom (Privacy), or telling whether you're talking to a bot or a person (Generative AI).
- Content Creation & Production: Making things yourself rather than only consuming, whether a document, an image, a video or a post. For example: building a presentation (Computer & Internet), removing personal data from a file before sharing it (Privacy), or disclosing that a text was written with AI (Generative AI).
The seven domains
Where those abilities matter today. This list is meant to evolve as technology does; AI and algorithms are recent additions.
- Computer & Internet: The everyday basics of devices, software and the web, on which everything else builds. For example: installing programs and organizing files (technical), choosing good search terms (information), making a document or presentation (creation).
- Privacy & Data Protection: What is collected about you, how you protect yourself, and what you give away, knowingly or not. For example: strong passwords and app permissions (technical), knowing your rights to access and erasure (information), judging when to share something personal (communication).
- News & Misinformation: How you inform yourself, and how you judge the quality of what you find. For example: running a reverse image search (technical), spotting manipulated images and one-sided feeds (information), sourcing your own posts properly (creation).
- Advertising: Recognizing advertising, including hidden and personalized forms, understanding how it works, and deciding as an informed consumer. For example: adjusting ad settings (technical), seeing through targeted ads (information), labelling your own promotional content (creation).
- Generative AI: Working with systems that generate text, images or video, and understanding how they work and where they fail. For example: using AI chatbots (technical), spotting synthetic content (information), disclosing where AI was involved (creation).
- Algorithms: How recommender systems and personalized feeds on YouTube, Instagram or TikTok decide what you see, and how to see through and steer them. For example: turning personalization off (technical), recognizing that results are ranked and not neutral (information), deliberately training your own feed (communication).
- Digital Well-Being: A healthy, self-determined way of using digital media. For example: using focus and time-limit features (technical), noticing when content is engineered to provoke anger or fear (information), responding well to cyberbullying (communication).
Strengths that carry over
These skills don't sit in separate boxes; they're connected, and strength in one place often ripples into another. A teenager who is fluent at communicating on social media may still need help carrying that fluency over to data privacy or to reading algorithms; an older adult may bring real caution and safety awareness but want support with the technical basics. Seen this way, no one is simply "literate" or "illiterate." Everyone has a profile (a personal pattern of strengths and gaps), and that profile is what we actually care about.
Why one size never fits all
This is also why generic, one-size-fits-all digital training tends to disappoint: it re-teaches what people already know and skips what they don't. Because every person carries a different map, education has to be tailored to match. Our framework is built to show exactly where a group shines and where it struggles, so support can be aimed at the specific gaps that matter most, in the domains where the stakes are highest. That, in the end, is what digital literacy is for: not just taking part, but taking part on your own terms, with confidence, safety, and purpose.